Patients' preferences for treatment outcomes for advanced non-small cell lung cancer: a conjoint analysis.
ABSTRACT Treatment decisions for advanced non-small cell lung cancer (NSCLC) are complex and require trade-offs between the benefits and risks experienced by patients. We evaluated the benefits that patients judged sufficient to compensate for the risks associated with therapy for NSCLC.
Participants with a self-reported diagnosis of NSCLC (n=100) were sampled from an online panel in the United Kingdom. Eligible and consenting participants then completed a self-administered online survey about their disease and their treatment preferences were assessed. This involved respondents choosing among systematically paired profiles that spanned eight attributes: progression-free survival [PFS], symptom severity, rash, diarrhoea, fatigue, nausea and vomiting, fever and infection, and mode of treatment administration (infusion and oral). A choice model was estimated using mixed-logit regression. Estimates of importance for each attribute level and attribute were then calculated and acceptable tradeoffs among attributes were explored.
A total of 89 respondents (73% male) completed all choice tasks appropriately. Increases in PFS together with improvements in symptom severity were judged most important and increased with PFS benefit - 4 months: 5.7; 95% CI: 3.5-7.9; 5 months: 7.1; 95% CI: 4.4-9.9; and 7 months: 10.0; 95% CI: 6.1-13.9. However, improvements in PFS were viewed as most beneficial when disease symptoms were mild and as detrimental when patients had severe symptoms. Fatigue (5.0; 95% CI: 2.7-7.3) was judged to be the most important risk, followed by diarrhoea (2.8; 95% CI: 0.7-4.9), nausea and vomiting (2.1; 95% CI: 0.1-4.1), fever and infection (2.1; 95% CI: 0.2-4.1), and rash (2.0; 95% CI: 0.2-3.9). Oral administration was preferred to infusion (1.8; 95% CI: 0.0-3.6). Patients with mild and moderate symptoms traded PFS for less risks or more convenience if the severe symptoms were not experienced.
This study demonstrates the value of conjoint analysis in the study of patient preferences for cancer treatments. In this small sample of patients with NSCLC from the UK, we demonstrate that the value of improvements in PFS is conditional upon the severity of disease symptoms; and that risks are valued differently.
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ABSTRACT: BACKGROUND: The rigorous elicitation of user needs is a crucial step for both medical device design and purchasing. However, user needs elicitation is often based on qualitative methods whose findings can be difficult to integrate into medical decision-making. This paper describes the application of AHP to elicit user needs for a new CT scanner for use in a public hospital. METHODS: AHP was used to design a hierarchy of 12 needs for a new CT scanner, grouped into 4 homogenous categories, and to prepare a paper questionnaire to investigate the relative priorities of these. The questionnaire was completed by 5 senior clinicians working in a variety of clinical specialisations and departments in the same Italian public hospital. RESULTS: Although safety and performance were considered the most important issues, user needs changed according to clinical scenario. For elective surgery, the five most important needs were: spatial resolution, processing software, radiation dose, patient monitoring, and contrast medium. For emergency, the top five most important needs were: patient monitoring, radiation dose, contrast medium control, speed run, spatial resolution. CONCLUSIONS: AHP effectively supported user need elicitation, helping to develop an analytic and intelligible framework of decision-making. User needs varied according to working scenario (elective versus emergency medicine) more than clinical specialization. This method should be considered by practitioners involved in decisions about new medical technology, whether that be during device design or before deciding whether to allocate budgets for new medical devices according to clinical functions or according to hospital department.BMC Medical Informatics and Decision Making 01/2013; 13(1):2. · 1.60 Impact Factor